Agent memory that scales from a chatbot to a fleet of agents.
mem0 is a memory solution mainly built for chatbots. It offers lightweight per-user personalization. Cognee is a self-evolving memory engine for many agents working at scale. Every interaction lives in one shared memory and each agent builds on what the others already learned.
What sets cognee apart.
From single-player to multi-player
mem0 works best in single-player mode: human-to-agent chat where it personalizes one user's conversation. Cognee is built for multi-player mode, agent-to-agent, where many agents share one memory.
One shared memory
If you want your chatbot to solve a customer's ticket end-to-end, cognee is the unified memory layer where every interaction and piece of feedback lives, so each agent builds on what the others already learned.
Cognee is more accurate
On the BEAM benchmark, mem0 scores 0.48 at 10M on top-200 retrieval. Cognee comes out ahead: 0.79 at 100k and 0.67 at 10M. Cognee's structured, durable memory holds state instead of degrading into competing near-duplicates.
mem0 remembers your user.Cognee remembers across your agents.
Per-user memory for chatbots
mem0 is a memory layer for AI assistants and chatbots. It uses an LLM to extract memories from a conversation, then retrieves them by blending semantic similarity with keyword and entity matching. It works best for remembering one user’s preferences and session facts across chats.
Shared memory for many agents
Cognee is a self-evolving memory engine built for agent-to-agent workflows at scale. Every interaction and piece of feedback lives in one shared memory, so each agent builds on what the others learned. The result is more accurate output and fewer mistakes. You can use it for chatbots, but it is even better at solving end-to-end customer escalations.
Cognee vs. mem0 at a glance.
LLM-extracted memories of a user's chats and preferences
Self-evolving memory that curates itself across many agents
Multi-signal retrieval: semantic and keyword (BM25) search with entity matching
Hybrid search with no separate index to build or keep in sync
Vector store with built-in entity linking
A single Postgres for graph, vectors, sessions, and metadata
Library, self-hosted server, or managed cloud
Local, managed cloud, or on the edge
Open source (Apache 2.0), plus a hosted platform
Open source (Apache 2.0), with memory you can export anytime via open COGX
Per-user chat memory for assistants and chatbots
From chatbot personalization to multi-agent settings on one shared memory
How we measure memory.
Both mem0 and Cognee publish results on BEAM, a long-horizon benchmark where evidence is scattered across many turns. mem0 also ran LoCoMo and LongMemEval. On BEAM at 10M scale, Cognee scores 67% versus mem0’s 48.6%.
mem0 uses single-pass retrieval with no agentic loops, optimising for token efficiency. Cognee is evaluated on BEAM at 100K and 10M token scales using open-source components, not a benchmark-specific system. Full methodology and reproducible benchmark code are publicly available.